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<div class="title">ndt.hpp</div>  </div>
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/*</span></div>
<div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Software License Agreement (BSD License)</span></div>
<div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment"> *  Point Cloud Library (PCL) - www.pointclouds.org</span></div>
<div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment"> *  Copyright (c) 2010-2011, Willow Garage, Inc.</span></div>
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<div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160; </div>
<div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;<span class="preprocessor">#ifndef PCL_REGISTRATION_NDT_IMPL_H_</span></div>
<div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;<span class="preprocessor">#define PCL_REGISTRATION_NDT_IMPL_H_</span></div>
<div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160; </div>
<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>Source, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>Target&gt;</div>
<div class="line"><a name="l00046"></a><span class="lineno"><a class="line" href="classpcl_1_1_normal_distributions_transform.html#af7bf146541e1f56bbc890fb21581b228">   46</a></span>&#160;<a class="code" href="classpcl_1_1_normal_distributions_transform.html#af7bf146541e1f56bbc890fb21581b228">pcl::NormalDistributionsTransform&lt;PointSource, PointTarget&gt;::NormalDistributionsTransform</a> () </div>
<div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;  : target_cells_ ()</div>
<div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;  , resolution_ (1.0f)</div>
<div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;  , step_size_ (0.1)</div>
<div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;  , outlier_ratio_ (0.55)</div>
<div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;  , gauss_d1_ ()</div>
<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;  , gauss_d2_ ()</div>
<div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;  , trans_probability_ ()</div>
<div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;  , j_ang_a_ (), j_ang_b_ (), j_ang_c_ (), j_ang_d_ (), j_ang_e_ (), j_ang_f_ (), j_ang_g_ (), j_ang_h_ ()</div>
<div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;  , h_ang_a2_ (), h_ang_a3_ (), h_ang_b2_ (), h_ang_b3_ (), h_ang_c2_ (), h_ang_c3_ (), h_ang_d1_ (), h_ang_d2_ ()</div>
<div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;  , h_ang_d3_ (), h_ang_e1_ (), h_ang_e2_ (), h_ang_e3_ (), h_ang_f1_ (), h_ang_f2_ (), h_ang_f3_ ()</div>
<div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;  , point_gradient_ ()</div>
<div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;  , point_hessian_ ()</div>
<div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;{</div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;  <a class="code" href="classpcl_1_1_registration.html#a1e493af70763e05bcaf5ecd0ed7be63d">reg_name_</a> = <span class="stringliteral">&quot;NormalDistributionsTransform&quot;</span>;</div>
<div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160; </div>
<div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;  <span class="keywordtype">double</span> gauss_c1, gauss_c2, gauss_d3;</div>
<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160; </div>
<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;  <span class="comment">// Initializes the guassian fitting parameters (eq. 6.8) [Magnusson 2009]</span></div>
<div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;  gauss_c1 = 10.0 * (1 - <a class="code" href="classpcl_1_1_normal_distributions_transform.html#a779d3b4c4f5181eb4f5ed6a660c66471">outlier_ratio_</a>);</div>
<div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;  gauss_c2 = <a class="code" href="classpcl_1_1_normal_distributions_transform.html#a779d3b4c4f5181eb4f5ed6a660c66471">outlier_ratio_</a> / pow (<a class="code" href="classpcl_1_1_normal_distributions_transform.html#a979b1ab50b52b130e0b29fda50e0afb0">resolution_</a>, 3);</div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;  gauss_d3 = -log (gauss_c2);</div>
<div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;  <a class="code" href="classpcl_1_1_normal_distributions_transform.html#a7b014c047dcf7fb8d5285e1cffeb125c">gauss_d1_</a> = -log ( gauss_c1 + gauss_c2 ) - gauss_d3;</div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;  gauss_d2_ = -2 * log ((-log ( gauss_c1 * exp ( -0.5 ) + gauss_c2 ) - gauss_d3) / <a class="code" href="classpcl_1_1_normal_distributions_transform.html#a7b014c047dcf7fb8d5285e1cffeb125c">gauss_d1_</a>);</div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160; </div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;  <a class="code" href="classpcl_1_1_registration.html#adbd6519634f433c0be2fd640c0c75108">transformation_epsilon_</a> = 0.1;</div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;  <a class="code" href="classpcl_1_1_registration.html#aa776d097d20137f2702a275d931989d2">max_iterations_</a> = 35;</div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;}</div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160; </div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>Source, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>Target&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00077"></a><span class="lineno"><a class="line" href="classpcl_1_1_normal_distributions_transform.html#a4abd14dda61865b6063868c3f3fc8845">   77</a></span>&#160;<a class="code" href="classpcl_1_1_normal_distributions_transform.html#a60ae727dd5185e7fc804b4d8de973a85">pcl::NormalDistributionsTransform&lt;PointSource, PointTarget&gt;::computeTransformation</a> (<a class="code" href="classpcl_1_1_point_cloud.html">PointCloudSource</a> &amp;output, <span class="keyword">const</span> Eigen::Matrix4f &amp;guess)</div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;{</div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;  nr_iterations_ = 0;</div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;  converged_ = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160; </div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;  <span class="keywordtype">double</span> gauss_c1, gauss_c2, gauss_d3;</div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160; </div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;  <span class="comment">// Initializes the guassian fitting parameters (eq. 6.8) [Magnusson 2009]</span></div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;  gauss_c1 = 10 * (1 - outlier_ratio_);</div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;  gauss_c2 = outlier_ratio_ / pow (resolution_, 3);</div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;  gauss_d3 = -log (gauss_c2);</div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;  gauss_d1_ = -log ( gauss_c1 + gauss_c2 ) - gauss_d3;</div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;  gauss_d2_ = -2 * log ((-log ( gauss_c1 * exp ( -0.5 ) + gauss_c2 ) - gauss_d3) / gauss_d1_);</div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160; </div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;  <span class="keywordflow">if</span> (guess != Eigen::Matrix4f::Identity ())</div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;  {</div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;    <span class="comment">// Initialise final transformation to the guessed one</span></div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;    final_transformation_ = guess;</div>
<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;    <span class="comment">// Apply guessed transformation prior to search for neighbours</span></div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;    <a class="code" href="group__common.html#ga52d532f7f2b4d7bba78d13701d3a33d8">transformPointCloud</a> (output, output, guess);</div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;  }</div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160; </div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;  <span class="comment">// Initialize Point Gradient and Hessian</span></div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;  point_gradient_.setZero ();</div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;  point_gradient_.block&lt;3, 3&gt;(0, 0).<a class="code" href="apps_2point__cloud__editor_2include_2pcl_2apps_2point__cloud__editor_2common_8h.html#ac51554cebbaae5040fd5bd3a55d1e6fe">setIdentity</a> ();</div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;  point_hessian_.setZero ();</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160; </div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;  Eigen::Transform&lt;float, 3, Eigen::Affine, Eigen::ColMajor&gt; eig_transformation;</div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;  eig_transformation.matrix () = final_transformation_;</div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160; </div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;  <span class="comment">// Convert initial guess matrix to 6 element transformation vector</span></div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;  Eigen::Matrix&lt;double, 6, 1&gt; p, delta_p, score_gradient;</div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;  Eigen::Vector3f init_translation = eig_transformation.translation ();</div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;  Eigen::Vector3f init_rotation = eig_transformation.rotation ().eulerAngles (0, 1, 2);</div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;  p &lt;&lt; init_translation (0), init_translation (1), init_translation (2),</div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;  init_rotation (0), init_rotation (1), init_rotation (2);</div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160; </div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;  Eigen::Matrix&lt;double, 6, 6&gt; hessian;</div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160; </div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;  <span class="keywordtype">double</span> score = 0;</div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;  <span class="keywordtype">double</span> delta_p_norm;</div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160; </div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;  <span class="comment">// Calculate derivates of initial transform vector, subsequent derivative calculations are done in the step length determination.</span></div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;  score = computeDerivatives (score_gradient, hessian, output, p);</div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160; </div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;  <span class="keywordflow">while</span> (!converged_)</div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;  {</div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;    <span class="comment">// Store previous transformation</span></div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;    previous_transformation_ = transformation_;</div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160; </div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;    <span class="comment">// Solve for decent direction using newton method, line 23 in Algorithm 2 [Magnusson 2009]</span></div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;    Eigen::JacobiSVD&lt;Eigen::Matrix&lt;double, 6, 6&gt; &gt; sv (hessian, Eigen::ComputeFullU | Eigen::ComputeFullV);</div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;    <span class="comment">// Negative for maximization as opposed to minimization</span></div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;    delta_p = sv.solve (-score_gradient);</div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160; </div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;    <span class="comment">//Calculate step length with guarnteed sufficient decrease [More, Thuente 1994]</span></div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;    delta_p_norm = delta_p.norm ();</div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160; </div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;    <span class="keywordflow">if</span> (delta_p_norm == 0 || delta_p_norm != delta_p_norm)</div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;    {</div>
<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;      trans_probability_ = score / <span class="keyword">static_cast&lt;</span><span class="keywordtype">double</span><span class="keyword">&gt;</span> (input_-&gt;points.size ());</div>
<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;      converged_ = delta_p_norm == delta_p_norm;</div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;      <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;    }</div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160; </div>
<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;    delta_p.normalize ();</div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;    delta_p_norm = computeStepLengthMT (p, delta_p, delta_p_norm, step_size_, transformation_epsilon_ / 2, score, score_gradient, hessian, output);</div>
<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;    delta_p *= delta_p_norm;</div>
<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160; </div>
<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160; </div>
<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;    transformation_ = (Eigen::Translation&lt;float, 3&gt; (<span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (delta_p (0)), <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (delta_p (1)), <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (delta_p (2))) *</div>
<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;                       Eigen::AngleAxis&lt;float&gt; (<span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (delta_p (3)), Eigen::Vector3f::UnitX ()) *</div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;                       Eigen::AngleAxis&lt;float&gt; (<span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (delta_p (4)), Eigen::Vector3f::UnitY ()) *</div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;                       Eigen::AngleAxis&lt;float&gt; (<span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (delta_p (5)), Eigen::Vector3f::UnitZ ())).matrix ();</div>
<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160; </div>
<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160; </div>
<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;    p = p + delta_p;</div>
<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160; </div>
<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;    <span class="comment">// Update Visualizer (untested)</span></div>
<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;    <span class="keywordflow">if</span> (update_visualizer_ != 0)</div>
<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;      update_visualizer_ (output, std::vector&lt;int&gt;(), *target_, std::vector&lt;int&gt;() );</div>
<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160; </div>
<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;    <span class="keywordflow">if</span> (nr_iterations_ &gt; max_iterations_ ||</div>
<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;        (nr_iterations_ &amp;&amp; (std::fabs (delta_p_norm) &lt; transformation_epsilon_)))</div>
<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;    {</div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;      converged_ = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;    }</div>
<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160; </div>
<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;    nr_iterations_++;</div>
<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160; </div>
<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;  }</div>
<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160; </div>
<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;  <span class="comment">// Store transformation probability.  The realtive differences within each scan registration are accurate</span></div>
<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;  <span class="comment">// but the normalization constants need to be modified for it to be globally accurate</span></div>
<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;  trans_probability_ = score / <span class="keyword">static_cast&lt;</span><span class="keywordtype">double</span><span class="keyword">&gt;</span> (input_-&gt;points.size ());</div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;}</div>
<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160; </div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>Source, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>Target&gt; <span class="keywordtype">double</span></div>
<div class="line"><a name="l00176"></a><span class="lineno"><a class="line" href="classpcl_1_1_normal_distributions_transform.html#a2eb79c026d9ec3bde70cf4b53377aa53">  176</a></span>&#160;<a class="code" href="classpcl_1_1_normal_distributions_transform.html#a2eb79c026d9ec3bde70cf4b53377aa53">pcl::NormalDistributionsTransform&lt;PointSource, PointTarget&gt;::computeDerivatives</a> (Eigen::Matrix&lt;double, 6, 1&gt; &amp;score_gradient,</div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;                                                                                 Eigen::Matrix&lt;double, 6, 6&gt; &amp;hessian,</div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;                                                                                 <a class="code" href="classpcl_1_1_point_cloud.html">PointCloudSource</a> &amp;trans_cloud,</div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;                                                                                 Eigen::Matrix&lt;double, 6, 1&gt; &amp;p,</div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;                                                                                 <span class="keywordtype">bool</span> compute_hessian)</div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;{</div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;  <span class="comment">// Original Point and Transformed Point</span></div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;  PointSource x_pt, x_trans_pt;</div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;  <span class="comment">// Original Point and Transformed Point (for math)</span></div>
<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;  Eigen::Vector3d x, x_trans;</div>
<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;  <span class="comment">// Occupied Voxel</span></div>
<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;  <a class="code" href="classpcl_1_1_normal_distributions_transform.html#ae241178695742c3cc138682b32f5f4b0">TargetGridLeafConstPtr</a> cell;</div>
<div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;  <span class="comment">// Inverse Covariance of Occupied Voxel</span></div>
<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;  Eigen::Matrix3d c_inv;</div>
<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160; </div>
<div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;  score_gradient.setZero ();</div>
<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;  hessian.setZero ();</div>
<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;  <span class="keywordtype">double</span> score = 0;</div>
<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160; </div>
<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;  <span class="comment">// Precompute Angular Derivatives (eq. 6.19 and 6.21)[Magnusson 2009]</span></div>
<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;  computeAngleDerivatives (p);</div>
<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160; </div>
<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;  <span class="comment">// Update gradient and hessian for each point, line 17 in Algorithm 2 [Magnusson 2009]</span></div>
<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> idx = 0; idx &lt; input_-&gt;points.size (); idx++)</div>
<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;  {</div>
<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;    x_trans_pt = trans_cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[idx];</div>
<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160; </div>
<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;    <span class="comment">// Find nieghbors (Radius search has been experimentally faster than direct neighbor checking.</span></div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;    std::vector&lt;TargetGridLeafConstPtr&gt; neighborhood;</div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;    std::vector&lt;float&gt; distances;</div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;    target_cells_.radiusSearch (x_trans_pt, resolution_, neighborhood, distances);</div>
<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160; </div>
<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;    <span class="keywordflow">for</span> (<span class="keyword">typename</span> std::vector&lt;TargetGridLeafConstPtr&gt;::iterator neighborhood_it = neighborhood.begin (); neighborhood_it != neighborhood.end (); neighborhood_it++)</div>
<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;    {</div>
<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;      cell = *neighborhood_it;</div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;      x_pt = input_-&gt;points[idx];</div>
<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;      x = Eigen::Vector3d (x_pt.x, x_pt.y, x_pt.z);</div>
<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160; </div>
<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;      x_trans = Eigen::Vector3d (x_trans_pt.x, x_trans_pt.y, x_trans_pt.z);</div>
<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160; </div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;      <span class="comment">// Denorm point, x_k&#39; in Equations 6.12 and 6.13 [Magnusson 2009]</span></div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;      x_trans -= cell-&gt;getMean ();</div>
<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;      <span class="comment">// Uses precomputed covariance for speed.</span></div>
<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;      c_inv = cell-&gt;getInverseCov ();</div>
<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160; </div>
<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;      <span class="comment">// Compute derivative of transform function w.r.t. transform vector, J_E and H_E in Equations 6.18 and 6.20 [Magnusson 2009]</span></div>
<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;      computePointDerivatives (x);</div>
<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;      <span class="comment">// Update score, gradient and hessian, lines 19-21 in Algorithm 2, according to Equations 6.10, 6.12 and 6.13, respectively [Magnusson 2009]</span></div>
<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;      score += updateDerivatives (score_gradient, hessian, x_trans, c_inv, compute_hessian);</div>
<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160; </div>
<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;    }</div>
<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;  }</div>
<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;  <span class="keywordflow">return</span> (score);</div>
<div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;}</div>
<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160; </div>
<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>Source, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>Target&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00233"></a><span class="lineno"><a class="line" href="classpcl_1_1_normal_distributions_transform.html#af99468f56f6bb95bef79193ab0b16205">  233</a></span>&#160;<a class="code" href="classpcl_1_1_normal_distributions_transform.html#af99468f56f6bb95bef79193ab0b16205">pcl::NormalDistributionsTransform&lt;PointSource, PointTarget&gt;::computeAngleDerivatives</a> (Eigen::Matrix&lt;double, 6, 1&gt; &amp;p, <span class="keywordtype">bool</span> compute_hessian)</div>
<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;{</div>
<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;  <span class="comment">// Simplified math for near 0 angles</span></div>
<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;  <span class="keywordtype">double</span> cx, cy, cz, sx, sy, sz;</div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;  <span class="keywordflow">if</span> (fabs (p (3)) &lt; 10e-5)</div>
<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;  {</div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;    <span class="comment">//p(3) = 0;</span></div>
<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;    cx = 1.0;</div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;    sx = 0.0;</div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;  }</div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;  {</div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;    cx = cos (p (3));</div>
<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;    sx = sin (p (3));</div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;  }</div>
<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;  <span class="keywordflow">if</span> (fabs (p (4)) &lt; 10e-5)</div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;  {</div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;    <span class="comment">//p(4) = 0;</span></div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;    cy = 1.0;</div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;    sy = 0.0;</div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;  }</div>
<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;  {</div>
<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;    cy = cos (p (4));</div>
<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;    sy = sin (p (4));</div>
<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;  }</div>
<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160; </div>
<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;  <span class="keywordflow">if</span> (fabs (p (5)) &lt; 10e-5)</div>
<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;  {</div>
<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;    <span class="comment">//p(5) = 0;</span></div>
<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;    cz = 1.0;</div>
<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;    sz = 0.0;</div>
<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;  }</div>
<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;  {</div>
<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;    cz = cos (p (5));</div>
<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;    sz = sin (p (5));</div>
<div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;  }</div>
<div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160; </div>
<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;  <span class="comment">// Precomputed angular gradiant components. Letters correspond to Equation 6.19 [Magnusson 2009]</span></div>
<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;  j_ang_a_ &lt;&lt; (-sx * sz + cx * sy * cz), (-sx * cz - cx * sy * sz), (-cx * cy);</div>
<div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;  j_ang_b_ &lt;&lt; (cx * sz + sx * sy * cz), (cx * cz - sx * sy * sz), (-sx * cy);</div>
<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;  j_ang_c_ &lt;&lt; (-sy * cz), sy * sz, cy;</div>
<div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;  j_ang_d_ &lt;&lt; sx * cy * cz, (-sx * cy * sz), sx * sy;</div>
<div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;  j_ang_e_ &lt;&lt; (-cx * cy * cz), cx * cy * sz, (-cx * sy);</div>
<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;  j_ang_f_ &lt;&lt; (-cy * sz), (-cy * cz), 0;</div>
<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;  j_ang_g_ &lt;&lt; (cx * cz - sx * sy * sz), (-cx * sz - sx * sy * cz), 0;</div>
<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;  j_ang_h_ &lt;&lt; (sx * cz + cx * sy * sz), (cx * sy * cz - sx * sz), 0;</div>
<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160; </div>
<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;  <span class="keywordflow">if</span> (compute_hessian)</div>
<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;  {</div>
<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;    <span class="comment">// Precomputed angular hessian components. Letters correspond to Equation 6.21 and numbers correspond to row index [Magnusson 2009]</span></div>
<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;    h_ang_a2_ &lt;&lt; (-cx * sz - sx * sy * cz), (-cx * cz + sx * sy * sz), sx * cy;</div>
<div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;    h_ang_a3_ &lt;&lt; (-sx * sz + cx * sy * cz), (-cx * sy * sz - sx * cz), (-cx * cy);</div>
<div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160; </div>
<div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;    h_ang_b2_ &lt;&lt; (cx * cy * cz), (-cx * cy * sz), (cx * sy);</div>
<div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;    h_ang_b3_ &lt;&lt; (sx * cy * cz), (-sx * cy * sz), (sx * sy);</div>
<div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160; </div>
<div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;    h_ang_c2_ &lt;&lt; (-sx * cz - cx * sy * sz), (sx * sz - cx * sy * cz), 0;</div>
<div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;    h_ang_c3_ &lt;&lt; (cx * cz - sx * sy * sz), (-sx * sy * cz - cx * sz), 0;</div>
<div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160; </div>
<div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;    h_ang_d1_ &lt;&lt; (-cy * cz), (cy * sz), (sy);</div>
<div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;    h_ang_d2_ &lt;&lt; (-sx * sy * cz), (sx * sy * sz), (sx * cy);</div>
<div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;    h_ang_d3_ &lt;&lt; (cx * sy * cz), (-cx * sy * sz), (-cx * cy);</div>
<div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160; </div>
<div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;    h_ang_e1_ &lt;&lt; (sy * sz), (sy * cz), 0;</div>
<div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;    h_ang_e2_ &lt;&lt; (-sx * cy * sz), (-sx * cy * cz), 0;</div>
<div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;    h_ang_e3_ &lt;&lt; (cx * cy * sz), (cx * cy * cz), 0;</div>
<div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160; </div>
<div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;    h_ang_f1_ &lt;&lt; (-cy * cz), (cy * sz), 0;</div>
<div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;    h_ang_f2_ &lt;&lt; (-cx * sz - sx * sy * cz), (-cx * cz + sx * sy * sz), 0;</div>
<div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;    h_ang_f3_ &lt;&lt; (-sx * sz + cx * sy * cz), (-cx * sy * sz - sx * cz), 0;</div>
<div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;  }</div>
<div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;}</div>
<div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160; </div>
<div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>Source, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>Target&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00310"></a><span class="lineno"><a class="line" href="classpcl_1_1_normal_distributions_transform.html#acdba743aa6ea3747e2fddeed10cc5ec1">  310</a></span>&#160;<a class="code" href="classpcl_1_1_normal_distributions_transform.html#acdba743aa6ea3747e2fddeed10cc5ec1">pcl::NormalDistributionsTransform&lt;PointSource, PointTarget&gt;::computePointDerivatives</a> (Eigen::Vector3d &amp;x, <span class="keywordtype">bool</span> compute_hessian)</div>
<div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;{</div>
<div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;  <span class="comment">// Calculate first derivative of Transformation Equation 6.17 w.r.t. transform vector p.</span></div>
<div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;  <span class="comment">// Derivative w.r.t. ith element of transform vector corresponds to column i, Equation 6.18 and 6.19 [Magnusson 2009]</span></div>
<div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;  point_gradient_ (1, 3) = x.dot (j_ang_a_);</div>
<div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;  point_gradient_ (2, 3) = x.dot (j_ang_b_);</div>
<div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;  point_gradient_ (0, 4) = x.dot (j_ang_c_);</div>
<div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;  point_gradient_ (1, 4) = x.dot (j_ang_d_);</div>
<div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;  point_gradient_ (2, 4) = x.dot (j_ang_e_);</div>
<div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;  point_gradient_ (0, 5) = x.dot (j_ang_f_);</div>
<div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;  point_gradient_ (1, 5) = x.dot (j_ang_g_);</div>
<div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;  point_gradient_ (2, 5) = x.dot (j_ang_h_);</div>
<div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160; </div>
<div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;  <span class="keywordflow">if</span> (compute_hessian)</div>
<div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;  {</div>
<div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;    <span class="comment">// Vectors from Equation 6.21 [Magnusson 2009]</span></div>
<div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;    Eigen::Vector3d a, b, c, d, e, f;</div>
<div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160; </div>
<div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;    a &lt;&lt; 0, x.dot (h_ang_a2_), x.dot (h_ang_a3_);</div>
<div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;    b &lt;&lt; 0, x.dot (h_ang_b2_), x.dot (h_ang_b3_);</div>
<div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;    c &lt;&lt; 0, x.dot (h_ang_c2_), x.dot (h_ang_c3_);</div>
<div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;    d &lt;&lt; x.dot (h_ang_d1_), x.dot (h_ang_d2_), x.dot (h_ang_d3_);</div>
<div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;    e &lt;&lt; x.dot (h_ang_e1_), x.dot (h_ang_e2_), x.dot (h_ang_e3_);</div>
<div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;    f &lt;&lt; x.dot (h_ang_f1_), x.dot (h_ang_f2_), x.dot (h_ang_f3_);</div>
<div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160; </div>
<div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;    <span class="comment">// Calculate second derivative of Transformation Equation 6.17 w.r.t. transform vector p.</span></div>
<div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;    <span class="comment">// Derivative w.r.t. ith and jth elements of transform vector corresponds to the 3x1 block matrix starting at (3i,j), Equation 6.20 and 6.21 [Magnusson 2009]</span></div>
<div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;    point_hessian_.block&lt;3, 1&gt;(9, 3) = a;</div>
<div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;    point_hessian_.block&lt;3, 1&gt;(12, 3) = b;</div>
<div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;    point_hessian_.block&lt;3, 1&gt;(15, 3) = c;</div>
<div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;    point_hessian_.block&lt;3, 1&gt;(9, 4) = b;</div>
<div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;    point_hessian_.block&lt;3, 1&gt;(12, 4) = d;</div>
<div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;    point_hessian_.block&lt;3, 1&gt;(15, 4) = e;</div>
<div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;    point_hessian_.block&lt;3, 1&gt;(9, 5) = c;</div>
<div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;    point_hessian_.block&lt;3, 1&gt;(12, 5) = e;</div>
<div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;    point_hessian_.block&lt;3, 1&gt;(15, 5) = f;</div>
<div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;  }</div>
<div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;}</div>
<div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160; </div>
<div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>Source, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>Target&gt; <span class="keywordtype">double</span></div>
<div class="line"><a name="l00351"></a><span class="lineno"><a class="line" href="classpcl_1_1_normal_distributions_transform.html#ad006c7315b1f52de25efc41183c5ed60">  351</a></span>&#160;<a class="code" href="classpcl_1_1_normal_distributions_transform.html#ad006c7315b1f52de25efc41183c5ed60">pcl::NormalDistributionsTransform&lt;PointSource, PointTarget&gt;::updateDerivatives</a> (Eigen::Matrix&lt;double, 6, 1&gt; &amp;score_gradient,</div>
<div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;                                                                                Eigen::Matrix&lt;double, 6, 6&gt; &amp;hessian,</div>
<div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;                                                                                Eigen::Vector3d &amp;x_trans, Eigen::Matrix3d &amp;c_inv,</div>
<div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;                                                                                <span class="keywordtype">bool</span> compute_hessian)</div>
<div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;{</div>
<div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;  Eigen::Vector3d cov_dxd_pi;</div>
<div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;  <span class="comment">// e^(-d_2/2 * (x_k - mu_k)^T Sigma_k^-1 (x_k - mu_k)) Equation 6.9 [Magnusson 2009]</span></div>
<div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;  <span class="keywordtype">double</span> e_x_cov_x = exp (-gauss_d2_ * x_trans.dot (c_inv * x_trans) / 2);</div>
<div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;  <span class="comment">// Calculate probability of transtormed points existance, Equation 6.9 [Magnusson 2009]</span></div>
<div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;  <span class="keywordtype">double</span> score_inc = -gauss_d1_ * e_x_cov_x;</div>
<div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160; </div>
<div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;  e_x_cov_x = gauss_d2_ * e_x_cov_x;</div>
<div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160; </div>
<div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;  <span class="comment">// Error checking for invalid values.</span></div>
<div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;  <span class="keywordflow">if</span> (e_x_cov_x &gt; 1 || e_x_cov_x &lt; 0 || e_x_cov_x != e_x_cov_x)</div>
<div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;    <span class="keywordflow">return</span> (0);</div>
<div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160; </div>
<div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;  <span class="comment">// Reusable portion of Equation 6.12 and 6.13 [Magnusson 2009]</span></div>
<div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;  e_x_cov_x *= gauss_d1_;</div>
<div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160; </div>
<div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160; </div>
<div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; 6; i++)</div>
<div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;  {</div>
<div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;    <span class="comment">// Sigma_k^-1 d(T(x,p))/dpi, Reusable portion of Equation 6.12 and 6.13 [Magnusson 2009]</span></div>
<div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;    cov_dxd_pi = c_inv * point_gradient_.col (i);</div>
<div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160; </div>
<div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;    <span class="comment">// Update gradient, Equation 6.12 [Magnusson 2009]</span></div>
<div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;    score_gradient (i) += x_trans.dot (cov_dxd_pi) * e_x_cov_x;</div>
<div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160; </div>
<div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;    <span class="keywordflow">if</span> (compute_hessian)</div>
<div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160;    {</div>
<div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j &lt; hessian.cols (); j++)</div>
<div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;      {</div>
<div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160;        <span class="comment">// Update hessian, Equation 6.13 [Magnusson 2009]</span></div>
<div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;        hessian (i, j) += e_x_cov_x * (-gauss_d2_ * x_trans.dot (cov_dxd_pi) * x_trans.dot (c_inv * point_gradient_.col (j)) +</div>
<div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;                                    x_trans.dot (c_inv * point_hessian_.block&lt;3, 1&gt;(3 * i, j)) +</div>
<div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;                                    point_gradient_.col (j).dot (cov_dxd_pi) );</div>
<div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;      }</div>
<div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;    }</div>
<div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;  }</div>
<div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160; </div>
<div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;  <span class="keywordflow">return</span> (score_inc);</div>
<div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;}</div>
<div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160; </div>
<div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>Source, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>Target&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00397"></a><span class="lineno"><a class="line" href="classpcl_1_1_normal_distributions_transform.html#a12a31cfee6372534d795c1f65fbfbd2d">  397</a></span>&#160;<a class="code" href="classpcl_1_1_normal_distributions_transform.html#a12a31cfee6372534d795c1f65fbfbd2d">pcl::NormalDistributionsTransform&lt;PointSource, PointTarget&gt;::computeHessian</a> (Eigen::Matrix&lt;double, 6, 6&gt; &amp;hessian,</div>
<div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160;                                                                             <a class="code" href="classpcl_1_1_point_cloud.html">PointCloudSource</a> &amp;trans_cloud, Eigen::Matrix&lt;double, 6, 1&gt; &amp;)</div>
<div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160;{</div>
<div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160;  <span class="comment">// Original Point and Transformed Point</span></div>
<div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;  PointSource x_pt, x_trans_pt;</div>
<div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;  <span class="comment">// Original Point and Transformed Point (for math)</span></div>
<div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160;  Eigen::Vector3d x, x_trans;</div>
<div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160;  <span class="comment">// Occupied Voxel</span></div>
<div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160;  <a class="code" href="classpcl_1_1_normal_distributions_transform.html#ae241178695742c3cc138682b32f5f4b0">TargetGridLeafConstPtr</a> cell;</div>
<div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;  <span class="comment">// Inverse Covariance of Occupied Voxel</span></div>
<div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;  Eigen::Matrix3d c_inv;</div>
<div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160; </div>
<div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160;  hessian.setZero ();</div>
<div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160; </div>
<div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;  <span class="comment">// Precompute Angular Derivatives unessisary because only used after regular derivative calculation</span></div>
<div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160; </div>
<div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;  <span class="comment">// Update hessian for each point, line 17 in Algorithm 2 [Magnusson 2009]</span></div>
<div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> idx = 0; idx &lt; input_-&gt;points.size (); idx++)</div>
<div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;  {</div>
<div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;    x_trans_pt = trans_cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[idx];</div>
<div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160; </div>
<div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;    <span class="comment">// Find nieghbors (Radius search has been experimentally faster than direct neighbor checking.</span></div>
<div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;    std::vector&lt;TargetGridLeafConstPtr&gt; neighborhood;</div>
<div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;    std::vector&lt;float&gt; distances;</div>
<div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;    target_cells_.radiusSearch (x_trans_pt, resolution_, neighborhood, distances);</div>
<div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160; </div>
<div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;    <span class="keywordflow">for</span> (<span class="keyword">typename</span> std::vector&lt;TargetGridLeafConstPtr&gt;::iterator neighborhood_it = neighborhood.begin (); neighborhood_it != neighborhood.end (); neighborhood_it++)</div>
<div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160;    {</div>
<div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;      cell = *neighborhood_it;</div>
<div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160; </div>
<div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;      {</div>
<div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160;        x_pt = input_-&gt;points[idx];</div>
<div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;        x = Eigen::Vector3d (x_pt.x, x_pt.y, x_pt.z);</div>
<div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160; </div>
<div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;        x_trans = Eigen::Vector3d (x_trans_pt.x, x_trans_pt.y, x_trans_pt.z);</div>
<div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160; </div>
<div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;        <span class="comment">// Denorm point, x_k&#39; in Equations 6.12 and 6.13 [Magnusson 2009]</span></div>
<div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;        x_trans -= cell-&gt;getMean ();</div>
<div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160;        <span class="comment">// Uses precomputed covariance for speed.</span></div>
<div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;        c_inv = cell-&gt;getInverseCov ();</div>
<div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160; </div>
<div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;        <span class="comment">// Compute derivative of transform function w.r.t. transform vector, J_E and H_E in Equations 6.18 and 6.20 [Magnusson 2009]</span></div>
<div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;        computePointDerivatives (x);</div>
<div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160;        <span class="comment">// Update hessian, lines 21 in Algorithm 2, according to Equations 6.10, 6.12 and 6.13, respectively [Magnusson 2009]</span></div>
<div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;        updateHessian (hessian, x_trans, c_inv);</div>
<div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;      }</div>
<div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;    }</div>
<div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160;  }</div>
<div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160;}</div>
<div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160; </div>
<div class="line"><a name="l00448"></a><span class="lineno">  448</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>Source, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>Target&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00449"></a><span class="lineno"><a class="line" href="classpcl_1_1_normal_distributions_transform.html#a9c6bd836040e430eea730cd6d16694f9">  449</a></span>&#160;<a class="code" href="classpcl_1_1_normal_distributions_transform.html#a9c6bd836040e430eea730cd6d16694f9">pcl::NormalDistributionsTransform&lt;PointSource, PointTarget&gt;::updateHessian</a> (Eigen::Matrix&lt;double, 6, 6&gt; &amp;hessian, Eigen::Vector3d &amp;x_trans, Eigen::Matrix3d &amp;c_inv)</div>
<div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160;{</div>
<div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;  Eigen::Vector3d cov_dxd_pi;</div>
<div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;  <span class="comment">// e^(-d_2/2 * (x_k - mu_k)^T Sigma_k^-1 (x_k - mu_k)) Equation 6.9 [Magnusson 2009]</span></div>
<div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;  <span class="keywordtype">double</span> e_x_cov_x = gauss_d2_ * exp (-gauss_d2_ * x_trans.dot (c_inv * x_trans) / 2);</div>
<div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160; </div>
<div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;  <span class="comment">// Error checking for invalid values.</span></div>
<div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;  <span class="keywordflow">if</span> (e_x_cov_x &gt; 1 || e_x_cov_x &lt; 0 || e_x_cov_x != e_x_cov_x)</div>
<div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160; </div>
<div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;  <span class="comment">// Reusable portion of Equation 6.12 and 6.13 [Magnusson 2009]</span></div>
<div class="line"><a name="l00460"></a><span class="lineno">  460</span>&#160;  e_x_cov_x *= gauss_d1_;</div>
<div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160; </div>
<div class="line"><a name="l00462"></a><span class="lineno">  462</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; 6; i++)</div>
<div class="line"><a name="l00463"></a><span class="lineno">  463</span>&#160;  {</div>
<div class="line"><a name="l00464"></a><span class="lineno">  464</span>&#160;    <span class="comment">// Sigma_k^-1 d(T(x,p))/dpi, Reusable portion of Equation 6.12 and 6.13 [Magnusson 2009]</span></div>
<div class="line"><a name="l00465"></a><span class="lineno">  465</span>&#160;    cov_dxd_pi = c_inv * point_gradient_.col (i);</div>
<div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160; </div>
<div class="line"><a name="l00467"></a><span class="lineno">  467</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j &lt; hessian.cols (); j++)</div>
<div class="line"><a name="l00468"></a><span class="lineno">  468</span>&#160;    {</div>
<div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160;      <span class="comment">// Update hessian, Equation 6.13 [Magnusson 2009]</span></div>
<div class="line"><a name="l00470"></a><span class="lineno">  470</span>&#160;      hessian (i, j) += e_x_cov_x * (-gauss_d2_ * x_trans.dot (cov_dxd_pi) * x_trans.dot (c_inv * point_gradient_.col (j)) +</div>
<div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160;                                  x_trans.dot (c_inv * point_hessian_.block&lt;3, 1&gt;(3 * i, j)) +</div>
<div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160;                                  point_gradient_.col (j).dot (cov_dxd_pi) );</div>
<div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;    }</div>
<div class="line"><a name="l00474"></a><span class="lineno">  474</span>&#160;  }</div>
<div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160; </div>
<div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;}</div>
<div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160; </div>
<div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>Source, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>Target&gt; <span class="keywordtype">bool</span></div>
<div class="line"><a name="l00480"></a><span class="lineno"><a class="line" href="classpcl_1_1_normal_distributions_transform.html#acef200272607a40dc9890481f11c2480">  480</a></span>&#160;<a class="code" href="classpcl_1_1_normal_distributions_transform.html#acef200272607a40dc9890481f11c2480">pcl::NormalDistributionsTransform&lt;PointSource, PointTarget&gt;::updateIntervalMT</a> (<span class="keywordtype">double</span> &amp;a_l, <span class="keywordtype">double</span> &amp;f_l, <span class="keywordtype">double</span> &amp;g_l,</div>
<div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;                                                                               <span class="keywordtype">double</span> &amp;a_u, <span class="keywordtype">double</span> &amp;f_u, <span class="keywordtype">double</span> &amp;g_u,</div>
<div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;                                                                               <span class="keywordtype">double</span> a_t, <span class="keywordtype">double</span> f_t, <span class="keywordtype">double</span> g_t)</div>
<div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;{</div>
<div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;  <span class="comment">// Case U1 in Update Algorithm and Case a in Modified Update Algorithm [More, Thuente 1994]</span></div>
<div class="line"><a name="l00485"></a><span class="lineno">  485</span>&#160;  <span class="keywordflow">if</span> (f_t &gt; f_l)</div>
<div class="line"><a name="l00486"></a><span class="lineno">  486</span>&#160;  {</div>
<div class="line"><a name="l00487"></a><span class="lineno">  487</span>&#160;    a_u = a_t;</div>
<div class="line"><a name="l00488"></a><span class="lineno">  488</span>&#160;    f_u = f_t;</div>
<div class="line"><a name="l00489"></a><span class="lineno">  489</span>&#160;    g_u = g_t;</div>
<div class="line"><a name="l00490"></a><span class="lineno">  490</span>&#160;    <span class="keywordflow">return</span> (<span class="keyword">false</span>);</div>
<div class="line"><a name="l00491"></a><span class="lineno">  491</span>&#160;  }</div>
<div class="line"><a name="l00492"></a><span class="lineno">  492</span>&#160;  <span class="comment">// Case U2 in Update Algorithm and Case b in Modified Update Algorithm [More, Thuente 1994]</span></div>
<div class="line"><a name="l00493"></a><span class="lineno">  493</span>&#160;  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00494"></a><span class="lineno">  494</span>&#160;  <span class="keywordflow">if</span> (g_t * (a_l - a_t) &gt; 0)</div>
<div class="line"><a name="l00495"></a><span class="lineno">  495</span>&#160;  {</div>
<div class="line"><a name="l00496"></a><span class="lineno">  496</span>&#160;    a_l = a_t;</div>
<div class="line"><a name="l00497"></a><span class="lineno">  497</span>&#160;    f_l = f_t;</div>
<div class="line"><a name="l00498"></a><span class="lineno">  498</span>&#160;    g_l = g_t;</div>
<div class="line"><a name="l00499"></a><span class="lineno">  499</span>&#160;    <span class="keywordflow">return</span> (<span class="keyword">false</span>);</div>
<div class="line"><a name="l00500"></a><span class="lineno">  500</span>&#160;  }</div>
<div class="line"><a name="l00501"></a><span class="lineno">  501</span>&#160;  <span class="comment">// Case U3 in Update Algorithm and Case c in Modified Update Algorithm [More, Thuente 1994]</span></div>
<div class="line"><a name="l00502"></a><span class="lineno">  502</span>&#160;  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00503"></a><span class="lineno">  503</span>&#160;  <span class="keywordflow">if</span> (g_t * (a_l - a_t) &lt; 0)</div>
<div class="line"><a name="l00504"></a><span class="lineno">  504</span>&#160;  {</div>
<div class="line"><a name="l00505"></a><span class="lineno">  505</span>&#160;    a_u = a_l;</div>
<div class="line"><a name="l00506"></a><span class="lineno">  506</span>&#160;    f_u = f_l;</div>
<div class="line"><a name="l00507"></a><span class="lineno">  507</span>&#160;    g_u = g_l;</div>
<div class="line"><a name="l00508"></a><span class="lineno">  508</span>&#160; </div>
<div class="line"><a name="l00509"></a><span class="lineno">  509</span>&#160;    a_l = a_t;</div>
<div class="line"><a name="l00510"></a><span class="lineno">  510</span>&#160;    f_l = f_t;</div>
<div class="line"><a name="l00511"></a><span class="lineno">  511</span>&#160;    g_l = g_t;</div>
<div class="line"><a name="l00512"></a><span class="lineno">  512</span>&#160;    <span class="keywordflow">return</span> (<span class="keyword">false</span>);</div>
<div class="line"><a name="l00513"></a><span class="lineno">  513</span>&#160;  }</div>
<div class="line"><a name="l00514"></a><span class="lineno">  514</span>&#160;  <span class="comment">// Interval Converged</span></div>
<div class="line"><a name="l00515"></a><span class="lineno">  515</span>&#160;  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00516"></a><span class="lineno">  516</span>&#160;    <span class="keywordflow">return</span> (<span class="keyword">true</span>);</div>
<div class="line"><a name="l00517"></a><span class="lineno">  517</span>&#160;}</div>
<div class="line"><a name="l00518"></a><span class="lineno">  518</span>&#160; </div>
<div class="line"><a name="l00520"></a><span class="lineno">  520</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>Source, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>Target&gt; <span class="keywordtype">double</span></div>
<div class="line"><a name="l00521"></a><span class="lineno"><a class="line" href="classpcl_1_1_normal_distributions_transform.html#a7ae4590ac0242cb320ea6f29e1b93ba6">  521</a></span>&#160;<a class="code" href="classpcl_1_1_normal_distributions_transform.html#a7ae4590ac0242cb320ea6f29e1b93ba6">pcl::NormalDistributionsTransform&lt;PointSource, PointTarget&gt;::trialValueSelectionMT</a> (<span class="keywordtype">double</span> a_l, <span class="keywordtype">double</span> f_l, <span class="keywordtype">double</span> g_l,</div>
<div class="line"><a name="l00522"></a><span class="lineno">  522</span>&#160;                                                                                    <span class="keywordtype">double</span> a_u, <span class="keywordtype">double</span> f_u, <span class="keywordtype">double</span> g_u,</div>
<div class="line"><a name="l00523"></a><span class="lineno">  523</span>&#160;                                                                                    <span class="keywordtype">double</span> a_t, <span class="keywordtype">double</span> f_t, <span class="keywordtype">double</span> g_t)</div>
<div class="line"><a name="l00524"></a><span class="lineno">  524</span>&#160;{</div>
<div class="line"><a name="l00525"></a><span class="lineno">  525</span>&#160;  <span class="comment">// Case 1 in Trial Value Selection [More, Thuente 1994]</span></div>
<div class="line"><a name="l00526"></a><span class="lineno">  526</span>&#160;  <span class="keywordflow">if</span> (f_t &gt; f_l)</div>
<div class="line"><a name="l00527"></a><span class="lineno">  527</span>&#160;  {</div>
<div class="line"><a name="l00528"></a><span class="lineno">  528</span>&#160;    <span class="comment">// Calculate the minimizer of the cubic that interpolates f_l, f_t, g_l and g_t</span></div>
<div class="line"><a name="l00529"></a><span class="lineno">  529</span>&#160;    <span class="comment">// Equation 2.4.52 [Sun, Yuan 2006]</span></div>
<div class="line"><a name="l00530"></a><span class="lineno">  530</span>&#160;    <span class="keywordtype">double</span> z = 3 * (f_t - f_l) / (a_t - a_l) - g_t - g_l;</div>
<div class="line"><a name="l00531"></a><span class="lineno">  531</span>&#160;    <span class="keywordtype">double</span> w = std::sqrt (z * z - g_t * g_l);</div>
<div class="line"><a name="l00532"></a><span class="lineno">  532</span>&#160;    <span class="comment">// Equation 2.4.56 [Sun, Yuan 2006]</span></div>
<div class="line"><a name="l00533"></a><span class="lineno">  533</span>&#160;    <span class="keywordtype">double</span> a_c = a_l + (a_t - a_l) * (w - g_l - z) / (g_t - g_l + 2 * w);</div>
<div class="line"><a name="l00534"></a><span class="lineno">  534</span>&#160; </div>
<div class="line"><a name="l00535"></a><span class="lineno">  535</span>&#160;    <span class="comment">// Calculate the minimizer of the quadratic that interpolates f_l, f_t and g_l</span></div>
<div class="line"><a name="l00536"></a><span class="lineno">  536</span>&#160;    <span class="comment">// Equation 2.4.2 [Sun, Yuan 2006]</span></div>
<div class="line"><a name="l00537"></a><span class="lineno">  537</span>&#160;    <span class="keywordtype">double</span> a_q = a_l - 0.5 * (a_l - a_t) * g_l / (g_l - (f_l - f_t) / (a_l - a_t));</div>
<div class="line"><a name="l00538"></a><span class="lineno">  538</span>&#160; </div>
<div class="line"><a name="l00539"></a><span class="lineno">  539</span>&#160;    <span class="keywordflow">if</span> (std::fabs (a_c - a_l) &lt; std::fabs (a_q - a_l))</div>
<div class="line"><a name="l00540"></a><span class="lineno">  540</span>&#160;      <span class="keywordflow">return</span> (a_c);</div>
<div class="line"><a name="l00541"></a><span class="lineno">  541</span>&#160;    <span class="keywordflow">else</span></div>
<div class="line"><a name="l00542"></a><span class="lineno">  542</span>&#160;      <span class="keywordflow">return</span> (0.5 * (a_q + a_c));</div>
<div class="line"><a name="l00543"></a><span class="lineno">  543</span>&#160;  }</div>
<div class="line"><a name="l00544"></a><span class="lineno">  544</span>&#160;  <span class="comment">// Case 2 in Trial Value Selection [More, Thuente 1994]</span></div>
<div class="line"><a name="l00545"></a><span class="lineno">  545</span>&#160;  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00546"></a><span class="lineno">  546</span>&#160;  <span class="keywordflow">if</span> (g_t * g_l &lt; 0)</div>
<div class="line"><a name="l00547"></a><span class="lineno">  547</span>&#160;  {</div>
<div class="line"><a name="l00548"></a><span class="lineno">  548</span>&#160;    <span class="comment">// Calculate the minimizer of the cubic that interpolates f_l, f_t, g_l and g_t</span></div>
<div class="line"><a name="l00549"></a><span class="lineno">  549</span>&#160;    <span class="comment">// Equation 2.4.52 [Sun, Yuan 2006]</span></div>
<div class="line"><a name="l00550"></a><span class="lineno">  550</span>&#160;    <span class="keywordtype">double</span> z = 3 * (f_t - f_l) / (a_t - a_l) - g_t - g_l;</div>
<div class="line"><a name="l00551"></a><span class="lineno">  551</span>&#160;    <span class="keywordtype">double</span> w = std::sqrt (z * z - g_t * g_l);</div>
<div class="line"><a name="l00552"></a><span class="lineno">  552</span>&#160;    <span class="comment">// Equation 2.4.56 [Sun, Yuan 2006]</span></div>
<div class="line"><a name="l00553"></a><span class="lineno">  553</span>&#160;    <span class="keywordtype">double</span> a_c = a_l + (a_t - a_l) * (w - g_l - z) / (g_t - g_l + 2 * w);</div>
<div class="line"><a name="l00554"></a><span class="lineno">  554</span>&#160; </div>
<div class="line"><a name="l00555"></a><span class="lineno">  555</span>&#160;    <span class="comment">// Calculate the minimizer of the quadratic that interpolates f_l, g_l and g_t</span></div>
<div class="line"><a name="l00556"></a><span class="lineno">  556</span>&#160;    <span class="comment">// Equation 2.4.5 [Sun, Yuan 2006]</span></div>
<div class="line"><a name="l00557"></a><span class="lineno">  557</span>&#160;    <span class="keywordtype">double</span> a_s = a_l - (a_l - a_t) / (g_l - g_t) * g_l;</div>
<div class="line"><a name="l00558"></a><span class="lineno">  558</span>&#160; </div>
<div class="line"><a name="l00559"></a><span class="lineno">  559</span>&#160;    <span class="keywordflow">if</span> (std::fabs (a_c - a_t) &gt;= std::fabs (a_s - a_t))</div>
<div class="line"><a name="l00560"></a><span class="lineno">  560</span>&#160;      <span class="keywordflow">return</span> (a_c);</div>
<div class="line"><a name="l00561"></a><span class="lineno">  561</span>&#160;    <span class="keywordflow">else</span></div>
<div class="line"><a name="l00562"></a><span class="lineno">  562</span>&#160;      <span class="keywordflow">return</span> (a_s);</div>
<div class="line"><a name="l00563"></a><span class="lineno">  563</span>&#160;  }</div>
<div class="line"><a name="l00564"></a><span class="lineno">  564</span>&#160;  <span class="comment">// Case 3 in Trial Value Selection [More, Thuente 1994]</span></div>
<div class="line"><a name="l00565"></a><span class="lineno">  565</span>&#160;  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00566"></a><span class="lineno">  566</span>&#160;  <span class="keywordflow">if</span> (std::fabs (g_t) &lt;= std::fabs (g_l))</div>
<div class="line"><a name="l00567"></a><span class="lineno">  567</span>&#160;  {</div>
<div class="line"><a name="l00568"></a><span class="lineno">  568</span>&#160;    <span class="comment">// Calculate the minimizer of the cubic that interpolates f_l, f_t, g_l and g_t</span></div>
<div class="line"><a name="l00569"></a><span class="lineno">  569</span>&#160;    <span class="comment">// Equation 2.4.52 [Sun, Yuan 2006]</span></div>
<div class="line"><a name="l00570"></a><span class="lineno">  570</span>&#160;    <span class="keywordtype">double</span> z = 3 * (f_t - f_l) / (a_t - a_l) - g_t - g_l;</div>
<div class="line"><a name="l00571"></a><span class="lineno">  571</span>&#160;    <span class="keywordtype">double</span> w = std::sqrt (z * z - g_t * g_l);</div>
<div class="line"><a name="l00572"></a><span class="lineno">  572</span>&#160;    <span class="keywordtype">double</span> a_c = a_l + (a_t - a_l) * (w - g_l - z) / (g_t - g_l + 2 * w);</div>
<div class="line"><a name="l00573"></a><span class="lineno">  573</span>&#160; </div>
<div class="line"><a name="l00574"></a><span class="lineno">  574</span>&#160;    <span class="comment">// Calculate the minimizer of the quadratic that interpolates g_l and g_t</span></div>
<div class="line"><a name="l00575"></a><span class="lineno">  575</span>&#160;    <span class="comment">// Equation 2.4.5 [Sun, Yuan 2006]</span></div>
<div class="line"><a name="l00576"></a><span class="lineno">  576</span>&#160;    <span class="keywordtype">double</span> a_s = a_l - (a_l - a_t) / (g_l - g_t) * g_l;</div>
<div class="line"><a name="l00577"></a><span class="lineno">  577</span>&#160; </div>
<div class="line"><a name="l00578"></a><span class="lineno">  578</span>&#160;    <span class="keywordtype">double</span> a_t_next;</div>
<div class="line"><a name="l00579"></a><span class="lineno">  579</span>&#160; </div>
<div class="line"><a name="l00580"></a><span class="lineno">  580</span>&#160;    <span class="keywordflow">if</span> (std::fabs (a_c - a_t) &lt; std::fabs (a_s - a_t))</div>
<div class="line"><a name="l00581"></a><span class="lineno">  581</span>&#160;      a_t_next = a_c;</div>
<div class="line"><a name="l00582"></a><span class="lineno">  582</span>&#160;    <span class="keywordflow">else</span></div>
<div class="line"><a name="l00583"></a><span class="lineno">  583</span>&#160;      a_t_next = a_s;</div>
<div class="line"><a name="l00584"></a><span class="lineno">  584</span>&#160; </div>
<div class="line"><a name="l00585"></a><span class="lineno">  585</span>&#160;    <span class="keywordflow">if</span> (a_t &gt; a_l)</div>
<div class="line"><a name="l00586"></a><span class="lineno">  586</span>&#160;      <span class="keywordflow">return</span> (std::min (a_t + 0.66 * (a_u - a_t), a_t_next));</div>
<div class="line"><a name="l00587"></a><span class="lineno">  587</span>&#160;    <span class="keywordflow">else</span></div>
<div class="line"><a name="l00588"></a><span class="lineno">  588</span>&#160;      <span class="keywordflow">return</span> (std::max (a_t + 0.66 * (a_u - a_t), a_t_next));</div>
<div class="line"><a name="l00589"></a><span class="lineno">  589</span>&#160;  }</div>
<div class="line"><a name="l00590"></a><span class="lineno">  590</span>&#160;  <span class="comment">// Case 4 in Trial Value Selection [More, Thuente 1994]</span></div>
<div class="line"><a name="l00591"></a><span class="lineno">  591</span>&#160;  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00592"></a><span class="lineno">  592</span>&#160;  {</div>
<div class="line"><a name="l00593"></a><span class="lineno">  593</span>&#160;    <span class="comment">// Calculate the minimizer of the cubic that interpolates f_u, f_t, g_u and g_t</span></div>
<div class="line"><a name="l00594"></a><span class="lineno">  594</span>&#160;    <span class="comment">// Equation 2.4.52 [Sun, Yuan 2006]</span></div>
<div class="line"><a name="l00595"></a><span class="lineno">  595</span>&#160;    <span class="keywordtype">double</span> z = 3 * (f_t - f_u) / (a_t - a_u) - g_t - g_u;</div>
<div class="line"><a name="l00596"></a><span class="lineno">  596</span>&#160;    <span class="keywordtype">double</span> w = std::sqrt (z * z - g_t * g_u);</div>
<div class="line"><a name="l00597"></a><span class="lineno">  597</span>&#160;    <span class="comment">// Equation 2.4.56 [Sun, Yuan 2006]</span></div>
<div class="line"><a name="l00598"></a><span class="lineno">  598</span>&#160;    <span class="keywordflow">return</span> (a_u + (a_t - a_u) * (w - g_u - z) / (g_t - g_u + 2 * w));</div>
<div class="line"><a name="l00599"></a><span class="lineno">  599</span>&#160;  }</div>
<div class="line"><a name="l00600"></a><span class="lineno">  600</span>&#160;}</div>
<div class="line"><a name="l00601"></a><span class="lineno">  601</span>&#160; </div>
<div class="line"><a name="l00603"></a><span class="lineno">  603</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>Source, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>Target&gt; <span class="keywordtype">double</span></div>
<div class="line"><a name="l00604"></a><span class="lineno"><a class="line" href="classpcl_1_1_normal_distributions_transform.html#a92103be9ce6dc6838d13353358daa852">  604</a></span>&#160;<a class="code" href="classpcl_1_1_normal_distributions_transform.html#a92103be9ce6dc6838d13353358daa852">pcl::NormalDistributionsTransform&lt;PointSource, PointTarget&gt;::computeStepLengthMT</a> (<span class="keyword">const</span> Eigen::Matrix&lt;double, 6, 1&gt; &amp;x, Eigen::Matrix&lt;double, 6, 1&gt; &amp;step_dir, <span class="keywordtype">double</span> step_init, <span class="keywordtype">double</span> step_max,</div>
<div class="line"><a name="l00605"></a><span class="lineno">  605</span>&#160;                                                                                  <span class="keywordtype">double</span> step_min, <span class="keywordtype">double</span> &amp;score, Eigen::Matrix&lt;double, 6, 1&gt; &amp;score_gradient, Eigen::Matrix&lt;double, 6, 6&gt; &amp;hessian,</div>
<div class="line"><a name="l00606"></a><span class="lineno">  606</span>&#160;                                                                                  <a class="code" href="classpcl_1_1_point_cloud.html">PointCloudSource</a> &amp;trans_cloud)</div>
<div class="line"><a name="l00607"></a><span class="lineno">  607</span>&#160;{</div>
<div class="line"><a name="l00608"></a><span class="lineno">  608</span>&#160;  <span class="comment">// Set the value of phi(0), Equation 1.3 [More, Thuente 1994]</span></div>
<div class="line"><a name="l00609"></a><span class="lineno">  609</span>&#160;  <span class="keywordtype">double</span> phi_0 = -score;</div>
<div class="line"><a name="l00610"></a><span class="lineno">  610</span>&#160;  <span class="comment">// Set the value of phi&#39;(0), Equation 1.3 [More, Thuente 1994]</span></div>
<div class="line"><a name="l00611"></a><span class="lineno">  611</span>&#160;  <span class="keywordtype">double</span> d_phi_0 = -(score_gradient.dot (step_dir));</div>
<div class="line"><a name="l00612"></a><span class="lineno">  612</span>&#160; </div>
<div class="line"><a name="l00613"></a><span class="lineno">  613</span>&#160;  Eigen::Matrix&lt;double, 6, 1&gt;  x_t;</div>
<div class="line"><a name="l00614"></a><span class="lineno">  614</span>&#160; </div>
<div class="line"><a name="l00615"></a><span class="lineno">  615</span>&#160;  <span class="keywordflow">if</span> (d_phi_0 &gt;= 0)</div>
<div class="line"><a name="l00616"></a><span class="lineno">  616</span>&#160;  {</div>
<div class="line"><a name="l00617"></a><span class="lineno">  617</span>&#160;    <span class="comment">// Not a decent direction</span></div>
<div class="line"><a name="l00618"></a><span class="lineno">  618</span>&#160;    <span class="keywordflow">if</span> (d_phi_0 == 0)</div>
<div class="line"><a name="l00619"></a><span class="lineno">  619</span>&#160;      <span class="keywordflow">return</span> 0;</div>
<div class="line"><a name="l00620"></a><span class="lineno">  620</span>&#160;    <span class="keywordflow">else</span></div>
<div class="line"><a name="l00621"></a><span class="lineno">  621</span>&#160;    {</div>
<div class="line"><a name="l00622"></a><span class="lineno">  622</span>&#160;      <span class="comment">// Reverse step direction and calculate optimal step.</span></div>
<div class="line"><a name="l00623"></a><span class="lineno">  623</span>&#160;      d_phi_0 *= -1;</div>
<div class="line"><a name="l00624"></a><span class="lineno">  624</span>&#160;      step_dir *= -1;</div>
<div class="line"><a name="l00625"></a><span class="lineno">  625</span>&#160; </div>
<div class="line"><a name="l00626"></a><span class="lineno">  626</span>&#160;    }</div>
<div class="line"><a name="l00627"></a><span class="lineno">  627</span>&#160;  }</div>
<div class="line"><a name="l00628"></a><span class="lineno">  628</span>&#160; </div>
<div class="line"><a name="l00629"></a><span class="lineno">  629</span>&#160;  <span class="comment">// The Search Algorithm for T(mu) [More, Thuente 1994]</span></div>
<div class="line"><a name="l00630"></a><span class="lineno">  630</span>&#160; </div>
<div class="line"><a name="l00631"></a><span class="lineno">  631</span>&#160;  <span class="keywordtype">int</span> max_step_iterations = 10;</div>
<div class="line"><a name="l00632"></a><span class="lineno">  632</span>&#160;  <span class="keywordtype">int</span> step_iterations = 0;</div>
<div class="line"><a name="l00633"></a><span class="lineno">  633</span>&#160; </div>
<div class="line"><a name="l00634"></a><span class="lineno">  634</span>&#160;  <span class="comment">// Sufficient decreace constant, Equation 1.1 [More, Thuete 1994]</span></div>
<div class="line"><a name="l00635"></a><span class="lineno">  635</span>&#160;  <span class="keywordtype">double</span> mu = 1.e-4;</div>
<div class="line"><a name="l00636"></a><span class="lineno">  636</span>&#160;  <span class="comment">// Curvature condition constant, Equation 1.2 [More, Thuete 1994]</span></div>
<div class="line"><a name="l00637"></a><span class="lineno">  637</span>&#160;  <span class="keywordtype">double</span> nu = 0.9;</div>
<div class="line"><a name="l00638"></a><span class="lineno">  638</span>&#160; </div>
<div class="line"><a name="l00639"></a><span class="lineno">  639</span>&#160;  <span class="comment">// Initial endpoints of Interval I,</span></div>
<div class="line"><a name="l00640"></a><span class="lineno">  640</span>&#160;  <span class="keywordtype">double</span> a_l = 0, a_u = 0;</div>
<div class="line"><a name="l00641"></a><span class="lineno">  641</span>&#160; </div>
<div class="line"><a name="l00642"></a><span class="lineno">  642</span>&#160;  <span class="comment">// Auxiliary function psi is used until I is determined ot be a closed interval, Equation 2.1 [More, Thuente 1994]</span></div>
<div class="line"><a name="l00643"></a><span class="lineno">  643</span>&#160;  <span class="keywordtype">double</span> f_l = auxilaryFunction_PsiMT (a_l, phi_0, phi_0, d_phi_0, mu);</div>
<div class="line"><a name="l00644"></a><span class="lineno">  644</span>&#160;  <span class="keywordtype">double</span> g_l = auxilaryFunction_dPsiMT (d_phi_0, d_phi_0, mu);</div>
<div class="line"><a name="l00645"></a><span class="lineno">  645</span>&#160; </div>
<div class="line"><a name="l00646"></a><span class="lineno">  646</span>&#160;  <span class="keywordtype">double</span> f_u = auxilaryFunction_PsiMT (a_u, phi_0, phi_0, d_phi_0, mu);</div>
<div class="line"><a name="l00647"></a><span class="lineno">  647</span>&#160;  <span class="keywordtype">double</span> g_u = auxilaryFunction_dPsiMT (d_phi_0, d_phi_0, mu);</div>
<div class="line"><a name="l00648"></a><span class="lineno">  648</span>&#160; </div>
<div class="line"><a name="l00649"></a><span class="lineno">  649</span>&#160;  <span class="comment">// Check used to allow More-Thuente step length calculation to be skipped by making step_min == step_max</span></div>
<div class="line"><a name="l00650"></a><span class="lineno">  650</span>&#160;  <span class="keywordtype">bool</span> interval_converged = (step_max - step_min) &gt; 0, open_interval = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00651"></a><span class="lineno">  651</span>&#160; </div>
<div class="line"><a name="l00652"></a><span class="lineno">  652</span>&#160;  <span class="keywordtype">double</span> a_t = step_init;</div>
<div class="line"><a name="l00653"></a><span class="lineno">  653</span>&#160;  a_t = std::min (a_t, step_max);</div>
<div class="line"><a name="l00654"></a><span class="lineno">  654</span>&#160;  a_t = std::max (a_t, step_min);</div>
<div class="line"><a name="l00655"></a><span class="lineno">  655</span>&#160; </div>
<div class="line"><a name="l00656"></a><span class="lineno">  656</span>&#160;  x_t = x + step_dir * a_t;</div>
<div class="line"><a name="l00657"></a><span class="lineno">  657</span>&#160; </div>
<div class="line"><a name="l00658"></a><span class="lineno">  658</span>&#160;  final_transformation_ = (Eigen::Translation&lt;float, 3&gt;(<span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (x_t (0)), <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (x_t (1)), <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (x_t (2))) *</div>
<div class="line"><a name="l00659"></a><span class="lineno">  659</span>&#160;                           Eigen::AngleAxis&lt;float&gt; (<span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (x_t (3)), Eigen::Vector3f::UnitX ()) *</div>
<div class="line"><a name="l00660"></a><span class="lineno">  660</span>&#160;                           Eigen::AngleAxis&lt;float&gt; (<span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (x_t (4)), Eigen::Vector3f::UnitY ()) *</div>
<div class="line"><a name="l00661"></a><span class="lineno">  661</span>&#160;                           Eigen::AngleAxis&lt;float&gt; (<span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (x_t (5)), Eigen::Vector3f::UnitZ ())).matrix ();</div>
<div class="line"><a name="l00662"></a><span class="lineno">  662</span>&#160; </div>
<div class="line"><a name="l00663"></a><span class="lineno">  663</span>&#160;  <span class="comment">// New transformed point cloud</span></div>
<div class="line"><a name="l00664"></a><span class="lineno">  664</span>&#160;  <a class="code" href="group__common.html#ga52d532f7f2b4d7bba78d13701d3a33d8">transformPointCloud</a> (*input_, trans_cloud, final_transformation_);</div>
<div class="line"><a name="l00665"></a><span class="lineno">  665</span>&#160; </div>
<div class="line"><a name="l00666"></a><span class="lineno">  666</span>&#160;  <span class="comment">// Updates score, gradient and hessian.  Hessian calculation is unessisary but testing showed that most step calculations use the</span></div>
<div class="line"><a name="l00667"></a><span class="lineno">  667</span>&#160;  <span class="comment">// initial step suggestion and recalculation the reusable portions of the hessian would intail more computation time.</span></div>
<div class="line"><a name="l00668"></a><span class="lineno">  668</span>&#160;  score = computeDerivatives (score_gradient, hessian, trans_cloud, x_t, <span class="keyword">true</span>);</div>
<div class="line"><a name="l00669"></a><span class="lineno">  669</span>&#160; </div>
<div class="line"><a name="l00670"></a><span class="lineno">  670</span>&#160;  <span class="comment">// Calculate phi(alpha_t)</span></div>
<div class="line"><a name="l00671"></a><span class="lineno">  671</span>&#160;  <span class="keywordtype">double</span> phi_t = -score;</div>
<div class="line"><a name="l00672"></a><span class="lineno">  672</span>&#160;  <span class="comment">// Calculate phi&#39;(alpha_t)</span></div>
<div class="line"><a name="l00673"></a><span class="lineno">  673</span>&#160;  <span class="keywordtype">double</span> d_phi_t = -(score_gradient.dot (step_dir));</div>
<div class="line"><a name="l00674"></a><span class="lineno">  674</span>&#160; </div>
<div class="line"><a name="l00675"></a><span class="lineno">  675</span>&#160;  <span class="comment">// Calculate psi(alpha_t)</span></div>
<div class="line"><a name="l00676"></a><span class="lineno">  676</span>&#160;  <span class="keywordtype">double</span> psi_t = auxilaryFunction_PsiMT (a_t, phi_t, phi_0, d_phi_0, mu);</div>
<div class="line"><a name="l00677"></a><span class="lineno">  677</span>&#160;  <span class="comment">// Calculate psi&#39;(alpha_t)</span></div>
<div class="line"><a name="l00678"></a><span class="lineno">  678</span>&#160;  <span class="keywordtype">double</span> d_psi_t = auxilaryFunction_dPsiMT (d_phi_t, d_phi_0, mu);</div>
<div class="line"><a name="l00679"></a><span class="lineno">  679</span>&#160; </div>
<div class="line"><a name="l00680"></a><span class="lineno">  680</span>&#160;  <span class="comment">// Iterate until max number of iterations, interval convergance or a value satisfies the sufficient decrease, Equation 1.1, and curvature condition, Equation 1.2 [More, Thuente 1994]</span></div>
<div class="line"><a name="l00681"></a><span class="lineno">  681</span>&#160;  <span class="keywordflow">while</span> (!interval_converged &amp;&amp; step_iterations &lt; max_step_iterations &amp;&amp; !(psi_t &lt;= 0 <span class="comment">/*Sufficient Decrease*/</span> &amp;&amp; d_phi_t &lt;= -nu * d_phi_0 <span class="comment">/*Curvature Condition*/</span>))</div>
<div class="line"><a name="l00682"></a><span class="lineno">  682</span>&#160;  {</div>
<div class="line"><a name="l00683"></a><span class="lineno">  683</span>&#160;    <span class="comment">// Use auxilary function if interval I is not closed</span></div>
<div class="line"><a name="l00684"></a><span class="lineno">  684</span>&#160;    <span class="keywordflow">if</span> (open_interval)</div>
<div class="line"><a name="l00685"></a><span class="lineno">  685</span>&#160;    {</div>
<div class="line"><a name="l00686"></a><span class="lineno">  686</span>&#160;      a_t = trialValueSelectionMT (a_l, f_l, g_l,</div>
<div class="line"><a name="l00687"></a><span class="lineno">  687</span>&#160;                                   a_u, f_u, g_u,</div>
<div class="line"><a name="l00688"></a><span class="lineno">  688</span>&#160;                                   a_t, psi_t, d_psi_t);</div>
<div class="line"><a name="l00689"></a><span class="lineno">  689</span>&#160;    }</div>
<div class="line"><a name="l00690"></a><span class="lineno">  690</span>&#160;    <span class="keywordflow">else</span></div>
<div class="line"><a name="l00691"></a><span class="lineno">  691</span>&#160;    {</div>
<div class="line"><a name="l00692"></a><span class="lineno">  692</span>&#160;      a_t = trialValueSelectionMT (a_l, f_l, g_l,</div>
<div class="line"><a name="l00693"></a><span class="lineno">  693</span>&#160;                                   a_u, f_u, g_u,</div>
<div class="line"><a name="l00694"></a><span class="lineno">  694</span>&#160;                                   a_t, phi_t, d_phi_t);</div>
<div class="line"><a name="l00695"></a><span class="lineno">  695</span>&#160;    }</div>
<div class="line"><a name="l00696"></a><span class="lineno">  696</span>&#160; </div>
<div class="line"><a name="l00697"></a><span class="lineno">  697</span>&#160;    a_t = std::min (a_t, step_max);</div>
<div class="line"><a name="l00698"></a><span class="lineno">  698</span>&#160;    a_t = std::max (a_t, step_min);</div>
<div class="line"><a name="l00699"></a><span class="lineno">  699</span>&#160; </div>
<div class="line"><a name="l00700"></a><span class="lineno">  700</span>&#160;    x_t = x + step_dir * a_t;</div>
<div class="line"><a name="l00701"></a><span class="lineno">  701</span>&#160; </div>
<div class="line"><a name="l00702"></a><span class="lineno">  702</span>&#160;    final_transformation_ = (Eigen::Translation&lt;float, 3&gt; (<span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (x_t (0)), <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (x_t (1)), <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (x_t (2))) *</div>
<div class="line"><a name="l00703"></a><span class="lineno">  703</span>&#160;                             Eigen::AngleAxis&lt;float&gt; (<span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (x_t (3)), Eigen::Vector3f::UnitX ()) *</div>
<div class="line"><a name="l00704"></a><span class="lineno">  704</span>&#160;                             Eigen::AngleAxis&lt;float&gt; (<span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (x_t (4)), Eigen::Vector3f::UnitY ()) *</div>
<div class="line"><a name="l00705"></a><span class="lineno">  705</span>&#160;                             Eigen::AngleAxis&lt;float&gt; (<span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (x_t (5)), Eigen::Vector3f::UnitZ ())).matrix ();</div>
<div class="line"><a name="l00706"></a><span class="lineno">  706</span>&#160; </div>
<div class="line"><a name="l00707"></a><span class="lineno">  707</span>&#160;    <span class="comment">// New transformed point cloud</span></div>
<div class="line"><a name="l00708"></a><span class="lineno">  708</span>&#160;    <span class="comment">// Done on final cloud to prevent wasted computation</span></div>
<div class="line"><a name="l00709"></a><span class="lineno">  709</span>&#160;    <a class="code" href="group__common.html#ga52d532f7f2b4d7bba78d13701d3a33d8">transformPointCloud</a> (*input_, trans_cloud, final_transformation_);</div>
<div class="line"><a name="l00710"></a><span class="lineno">  710</span>&#160; </div>
<div class="line"><a name="l00711"></a><span class="lineno">  711</span>&#160;    <span class="comment">// Updates score, gradient. Values stored to prevent wasted computation.</span></div>
<div class="line"><a name="l00712"></a><span class="lineno">  712</span>&#160;    score = computeDerivatives (score_gradient, hessian, trans_cloud, x_t, <span class="keyword">false</span>);</div>
<div class="line"><a name="l00713"></a><span class="lineno">  713</span>&#160; </div>
<div class="line"><a name="l00714"></a><span class="lineno">  714</span>&#160;    <span class="comment">// Calculate phi(alpha_t+)</span></div>
<div class="line"><a name="l00715"></a><span class="lineno">  715</span>&#160;    phi_t = -score;</div>
<div class="line"><a name="l00716"></a><span class="lineno">  716</span>&#160;    <span class="comment">// Calculate phi&#39;(alpha_t+)</span></div>
<div class="line"><a name="l00717"></a><span class="lineno">  717</span>&#160;    d_phi_t = -(score_gradient.dot (step_dir));</div>
<div class="line"><a name="l00718"></a><span class="lineno">  718</span>&#160; </div>
<div class="line"><a name="l00719"></a><span class="lineno">  719</span>&#160;    <span class="comment">// Calculate psi(alpha_t+)</span></div>
<div class="line"><a name="l00720"></a><span class="lineno">  720</span>&#160;    psi_t = auxilaryFunction_PsiMT (a_t, phi_t, phi_0, d_phi_0, mu);</div>
<div class="line"><a name="l00721"></a><span class="lineno">  721</span>&#160;    <span class="comment">// Calculate psi&#39;(alpha_t+)</span></div>
<div class="line"><a name="l00722"></a><span class="lineno">  722</span>&#160;    d_psi_t = auxilaryFunction_dPsiMT (d_phi_t, d_phi_0, mu);</div>
<div class="line"><a name="l00723"></a><span class="lineno">  723</span>&#160; </div>
<div class="line"><a name="l00724"></a><span class="lineno">  724</span>&#160;    <span class="comment">// Check if I is now a closed interval</span></div>
<div class="line"><a name="l00725"></a><span class="lineno">  725</span>&#160;    <span class="keywordflow">if</span> (open_interval &amp;&amp; (psi_t &lt;= 0 &amp;&amp; d_psi_t &gt;= 0))</div>
<div class="line"><a name="l00726"></a><span class="lineno">  726</span>&#160;    {</div>
<div class="line"><a name="l00727"></a><span class="lineno">  727</span>&#160;      open_interval = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00728"></a><span class="lineno">  728</span>&#160; </div>
<div class="line"><a name="l00729"></a><span class="lineno">  729</span>&#160;      <span class="comment">// Converts f_l and g_l from psi to phi</span></div>
<div class="line"><a name="l00730"></a><span class="lineno">  730</span>&#160;      f_l = f_l + phi_0 - mu * d_phi_0 * a_l;</div>
<div class="line"><a name="l00731"></a><span class="lineno">  731</span>&#160;      g_l = g_l + mu * d_phi_0;</div>
<div class="line"><a name="l00732"></a><span class="lineno">  732</span>&#160; </div>
<div class="line"><a name="l00733"></a><span class="lineno">  733</span>&#160;      <span class="comment">// Converts f_u and g_u from psi to phi</span></div>
<div class="line"><a name="l00734"></a><span class="lineno">  734</span>&#160;      f_u = f_u + phi_0 - mu * d_phi_0 * a_u;</div>
<div class="line"><a name="l00735"></a><span class="lineno">  735</span>&#160;      g_u = g_u + mu * d_phi_0;</div>
<div class="line"><a name="l00736"></a><span class="lineno">  736</span>&#160;    }</div>
<div class="line"><a name="l00737"></a><span class="lineno">  737</span>&#160; </div>
<div class="line"><a name="l00738"></a><span class="lineno">  738</span>&#160;    <span class="keywordflow">if</span> (open_interval)</div>
<div class="line"><a name="l00739"></a><span class="lineno">  739</span>&#160;    {</div>
<div class="line"><a name="l00740"></a><span class="lineno">  740</span>&#160;      <span class="comment">// Update interval end points using Updating Algorithm [More, Thuente 1994]</span></div>
<div class="line"><a name="l00741"></a><span class="lineno">  741</span>&#160;      interval_converged = updateIntervalMT (a_l, f_l, g_l,</div>
<div class="line"><a name="l00742"></a><span class="lineno">  742</span>&#160;                                             a_u, f_u, g_u,</div>
<div class="line"><a name="l00743"></a><span class="lineno">  743</span>&#160;                                             a_t, psi_t, d_psi_t);</div>
<div class="line"><a name="l00744"></a><span class="lineno">  744</span>&#160;    }</div>
<div class="line"><a name="l00745"></a><span class="lineno">  745</span>&#160;    <span class="keywordflow">else</span></div>
<div class="line"><a name="l00746"></a><span class="lineno">  746</span>&#160;    {</div>
<div class="line"><a name="l00747"></a><span class="lineno">  747</span>&#160;      <span class="comment">// Update interval end points using Modified Updating Algorithm [More, Thuente 1994]</span></div>
<div class="line"><a name="l00748"></a><span class="lineno">  748</span>&#160;      interval_converged = updateIntervalMT (a_l, f_l, g_l,</div>
<div class="line"><a name="l00749"></a><span class="lineno">  749</span>&#160;                                             a_u, f_u, g_u,</div>
<div class="line"><a name="l00750"></a><span class="lineno">  750</span>&#160;                                             a_t, phi_t, d_phi_t);</div>
<div class="line"><a name="l00751"></a><span class="lineno">  751</span>&#160;    }</div>
<div class="line"><a name="l00752"></a><span class="lineno">  752</span>&#160; </div>
<div class="line"><a name="l00753"></a><span class="lineno">  753</span>&#160;    step_iterations++;</div>
<div class="line"><a name="l00754"></a><span class="lineno">  754</span>&#160;  }</div>
<div class="line"><a name="l00755"></a><span class="lineno">  755</span>&#160; </div>
<div class="line"><a name="l00756"></a><span class="lineno">  756</span>&#160;  <span class="comment">// If inner loop was run then hessian needs to be calculated.</span></div>
<div class="line"><a name="l00757"></a><span class="lineno">  757</span>&#160;  <span class="comment">// Hessian is unnessisary for step length determination but gradients are required</span></div>
<div class="line"><a name="l00758"></a><span class="lineno">  758</span>&#160;  <span class="comment">// so derivative and transform data is stored for the next iteration.</span></div>
<div class="line"><a name="l00759"></a><span class="lineno">  759</span>&#160;  <span class="keywordflow">if</span> (step_iterations)</div>
<div class="line"><a name="l00760"></a><span class="lineno">  760</span>&#160;    computeHessian (hessian, trans_cloud, x_t);</div>
<div class="line"><a name="l00761"></a><span class="lineno">  761</span>&#160; </div>
<div class="line"><a name="l00762"></a><span class="lineno">  762</span>&#160;  <span class="keywordflow">return</span> (a_t);</div>
<div class="line"><a name="l00763"></a><span class="lineno">  763</span>&#160;}</div>
<div class="line"><a name="l00764"></a><span class="lineno">  764</span>&#160; </div>
<div class="line"><a name="l00765"></a><span class="lineno">  765</span>&#160;<span class="preprocessor">#endif </span><span class="comment">// PCL_REGISTRATION_NDT_IMPL_H_</span></div>
<div class="ttc" id="aapps_2point__cloud__editor_2include_2pcl_2apps_2point__cloud__editor_2common_8h_html_ac51554cebbaae5040fd5bd3a55d1e6fe"><div class="ttname"><a href="apps_2point__cloud__editor_2include_2pcl_2apps_2point__cloud__editor_2common_8h.html#ac51554cebbaae5040fd5bd3a55d1e6fe">setIdentity</a></div><div class="ttdeci">void setIdentity(float *matrix)</div><div class="ttdoc">Sets an array representing a 4x4 matrix to the identity</div></div>
<div class="ttc" id="aclasspcl_1_1_normal_distributions_transform_html_a12a31cfee6372534d795c1f65fbfbd2d"><div class="ttname"><a href="classpcl_1_1_normal_distributions_transform.html#a12a31cfee6372534d795c1f65fbfbd2d">pcl::NormalDistributionsTransform::computeHessian</a></div><div class="ttdeci">void computeHessian(Eigen::Matrix&lt; double, 6, 6 &gt; &amp;hessian, PointCloudSource &amp;trans_cloud, Eigen::Matrix&lt; double, 6, 1 &gt; &amp;p)</div><div class="ttdoc">Compute hessian of probability function w.r.t. the transformation vector.</div><div class="ttdef"><b>Definition:</b> ndt.hpp:397</div></div>
<div class="ttc" id="aclasspcl_1_1_normal_distributions_transform_html_a2eb79c026d9ec3bde70cf4b53377aa53"><div class="ttname"><a href="classpcl_1_1_normal_distributions_transform.html#a2eb79c026d9ec3bde70cf4b53377aa53">pcl::NormalDistributionsTransform::computeDerivatives</a></div><div class="ttdeci">double computeDerivatives(Eigen::Matrix&lt; double, 6, 1 &gt; &amp;score_gradient, Eigen::Matrix&lt; double, 6, 6 &gt; &amp;hessian, PointCloudSource &amp;trans_cloud, Eigen::Matrix&lt; double, 6, 1 &gt; &amp;p, bool compute_hessian=true)</div><div class="ttdoc">Compute derivatives of probability function w.r.t. the transformation vector.</div><div class="ttdef"><b>Definition:</b> ndt.hpp:176</div></div>
<div class="ttc" id="aclasspcl_1_1_normal_distributions_transform_html_a60ae727dd5185e7fc804b4d8de973a85"><div class="ttname"><a href="classpcl_1_1_normal_distributions_transform.html#a60ae727dd5185e7fc804b4d8de973a85">pcl::NormalDistributionsTransform::computeTransformation</a></div><div class="ttdeci">virtual void computeTransformation(PointCloudSource &amp;output)</div><div class="ttdoc">Estimate the transformation and returns the transformed source (input) as output.</div><div class="ttdef"><b>Definition:</b> ndt.h:238</div></div>
<div class="ttc" id="aclasspcl_1_1_normal_distributions_transform_html_a779d3b4c4f5181eb4f5ed6a660c66471"><div class="ttname"><a href="classpcl_1_1_normal_distributions_transform.html#a779d3b4c4f5181eb4f5ed6a660c66471">pcl::NormalDistributionsTransform::outlier_ratio_</a></div><div class="ttdeci">double outlier_ratio_</div><div class="ttdoc">The ratio of outliers of points w.r.t. a normal distribution, Equation 6.7 [Magnusson 2009].</div><div class="ttdef"><b>Definition:</b> ndt.h:429</div></div>
<div class="ttc" id="aclasspcl_1_1_normal_distributions_transform_html_a7ae4590ac0242cb320ea6f29e1b93ba6"><div class="ttname"><a href="classpcl_1_1_normal_distributions_transform.html#a7ae4590ac0242cb320ea6f29e1b93ba6">pcl::NormalDistributionsTransform::trialValueSelectionMT</a></div><div class="ttdeci">double trialValueSelectionMT(double a_l, double f_l, double g_l, double a_u, double f_u, double g_u, double a_t, double f_t, double g_t)</div><div class="ttdoc">Select new trial value for More-Thuente method.</div><div class="ttdef"><b>Definition:</b> ndt.hpp:521</div></div>
<div class="ttc" id="aclasspcl_1_1_normal_distributions_transform_html_a7b014c047dcf7fb8d5285e1cffeb125c"><div class="ttname"><a href="classpcl_1_1_normal_distributions_transform.html#a7b014c047dcf7fb8d5285e1cffeb125c">pcl::NormalDistributionsTransform::gauss_d1_</a></div><div class="ttdeci">double gauss_d1_</div><div class="ttdoc">The normalization constants used fit the point distribution to a normal distribution,...</div><div class="ttdef"><b>Definition:</b> ndt.h:432</div></div>
<div class="ttc" id="aclasspcl_1_1_normal_distributions_transform_html_a92103be9ce6dc6838d13353358daa852"><div class="ttname"><a href="classpcl_1_1_normal_distributions_transform.html#a92103be9ce6dc6838d13353358daa852">pcl::NormalDistributionsTransform::computeStepLengthMT</a></div><div class="ttdeci">double computeStepLengthMT(const Eigen::Matrix&lt; double, 6, 1 &gt; &amp;x, Eigen::Matrix&lt; double, 6, 1 &gt; &amp;step_dir, double step_init, double step_max, double step_min, double &amp;score, Eigen::Matrix&lt; double, 6, 1 &gt; &amp;score_gradient, Eigen::Matrix&lt; double, 6, 6 &gt; &amp;hessian, PointCloudSource &amp;trans_cloud)</div><div class="ttdoc">Compute line search step length and update transform and probability derivatives using More-Thuente m...</div><div class="ttdef"><b>Definition:</b> ndt.hpp:604</div></div>
<div class="ttc" id="aclasspcl_1_1_normal_distributions_transform_html_a979b1ab50b52b130e0b29fda50e0afb0"><div class="ttname"><a href="classpcl_1_1_normal_distributions_transform.html#a979b1ab50b52b130e0b29fda50e0afb0">pcl::NormalDistributionsTransform::resolution_</a></div><div class="ttdeci">float resolution_</div><div class="ttdoc">The side length of voxels.</div><div class="ttdef"><b>Definition:</b> ndt.h:423</div></div>
<div class="ttc" id="aclasspcl_1_1_normal_distributions_transform_html_a9c6bd836040e430eea730cd6d16694f9"><div class="ttname"><a href="classpcl_1_1_normal_distributions_transform.html#a9c6bd836040e430eea730cd6d16694f9">pcl::NormalDistributionsTransform::updateHessian</a></div><div class="ttdeci">void updateHessian(Eigen::Matrix&lt; double, 6, 6 &gt; &amp;hessian, Eigen::Vector3d &amp;x_trans, Eigen::Matrix3d &amp;c_inv)</div><div class="ttdoc">Compute individual point contirbutions to hessian of probability function w.r.t. the transformation v...</div><div class="ttdef"><b>Definition:</b> ndt.hpp:449</div></div>
<div class="ttc" id="aclasspcl_1_1_normal_distributions_transform_html_acdba743aa6ea3747e2fddeed10cc5ec1"><div class="ttname"><a href="classpcl_1_1_normal_distributions_transform.html#acdba743aa6ea3747e2fddeed10cc5ec1">pcl::NormalDistributionsTransform::computePointDerivatives</a></div><div class="ttdeci">void computePointDerivatives(Eigen::Vector3d &amp;x, bool compute_hessian=true)</div><div class="ttdoc">Compute point derivatives.</div><div class="ttdef"><b>Definition:</b> ndt.hpp:310</div></div>
<div class="ttc" id="aclasspcl_1_1_normal_distributions_transform_html_acef200272607a40dc9890481f11c2480"><div class="ttname"><a href="classpcl_1_1_normal_distributions_transform.html#acef200272607a40dc9890481f11c2480">pcl::NormalDistributionsTransform::updateIntervalMT</a></div><div class="ttdeci">bool updateIntervalMT(double &amp;a_l, double &amp;f_l, double &amp;g_l, double &amp;a_u, double &amp;f_u, double &amp;g_u, double a_t, double f_t, double g_t)</div><div class="ttdoc">Update interval of possible step lengths for More-Thuente method,  in More-Thuente (1994)</div><div class="ttdef"><b>Definition:</b> ndt.hpp:480</div></div>
<div class="ttc" id="aclasspcl_1_1_normal_distributions_transform_html_ad006c7315b1f52de25efc41183c5ed60"><div class="ttname"><a href="classpcl_1_1_normal_distributions_transform.html#ad006c7315b1f52de25efc41183c5ed60">pcl::NormalDistributionsTransform::updateDerivatives</a></div><div class="ttdeci">double updateDerivatives(Eigen::Matrix&lt; double, 6, 1 &gt; &amp;score_gradient, Eigen::Matrix&lt; double, 6, 6 &gt; &amp;hessian, Eigen::Vector3d &amp;x_trans, Eigen::Matrix3d &amp;c_inv, bool compute_hessian=true)</div><div class="ttdoc">Compute individual point contirbutions to derivatives of probability function w.r....</div><div class="ttdef"><b>Definition:</b> ndt.hpp:351</div></div>
<div class="ttc" id="aclasspcl_1_1_normal_distributions_transform_html_ae241178695742c3cc138682b32f5f4b0"><div class="ttname"><a href="classpcl_1_1_normal_distributions_transform.html#ae241178695742c3cc138682b32f5f4b0">pcl::NormalDistributionsTransform::TargetGridLeafConstPtr</a></div><div class="ttdeci">TargetGrid::LeafConstPtr TargetGridLeafConstPtr</div><div class="ttdoc">Typename of const pointer to searchable voxel grid leaf.</div><div class="ttdef"><b>Definition:</b> ndt.h:85</div></div>
<div class="ttc" id="aclasspcl_1_1_normal_distributions_transform_html_af7bf146541e1f56bbc890fb21581b228"><div class="ttname"><a href="classpcl_1_1_normal_distributions_transform.html#af7bf146541e1f56bbc890fb21581b228">pcl::NormalDistributionsTransform::NormalDistributionsTransform</a></div><div class="ttdeci">NormalDistributionsTransform()</div><div class="ttdoc">Constructor. Sets outlier_ratio_ to 0.35, step_size_ to 0.05 and resolution_ to 1....</div><div class="ttdef"><b>Definition:</b> ndt.hpp:46</div></div>
<div class="ttc" id="aclasspcl_1_1_normal_distributions_transform_html_af99468f56f6bb95bef79193ab0b16205"><div class="ttname"><a href="classpcl_1_1_normal_distributions_transform.html#af99468f56f6bb95bef79193ab0b16205">pcl::NormalDistributionsTransform::computeAngleDerivatives</a></div><div class="ttdeci">void computeAngleDerivatives(Eigen::Matrix&lt; double, 6, 1 &gt; &amp;p, bool compute_hessian=true)</div><div class="ttdoc">Precompute anglular components of derivatives.</div><div class="ttdef"><b>Definition:</b> ndt.hpp:233</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html"><div class="ttname"><a href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt; PointSource &gt;</a></div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_af16a62638198313b9c093127c492c884"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">pcl::PointCloud::points</a></div><div class="ttdeci">std::vector&lt; PointT, Eigen::aligned_allocator&lt; PointT &gt; &gt; points</div><div class="ttdoc">The point data.</div><div class="ttdef"><b>Definition:</b> point_cloud.h:410</div></div>
<div class="ttc" id="aclasspcl_1_1_registration_html_a1e493af70763e05bcaf5ecd0ed7be63d"><div class="ttname"><a href="classpcl_1_1_registration.html#a1e493af70763e05bcaf5ecd0ed7be63d">pcl::Registration::reg_name_</a></div><div class="ttdeci">std::string reg_name_</div><div class="ttdoc">The registration method name.</div><div class="ttdef"><b>Definition:</b> registration.h:482</div></div>
<div class="ttc" id="aclasspcl_1_1_registration_html_aa776d097d20137f2702a275d931989d2"><div class="ttname"><a href="classpcl_1_1_registration.html#aa776d097d20137f2702a275d931989d2">pcl::Registration::max_iterations_</a></div><div class="ttdeci">int max_iterations_</div><div class="ttdoc">The maximum number of iterations the internal optimization should run for. The default value is 10.</div><div class="ttdef"><b>Definition:</b> registration.h:496</div></div>
<div class="ttc" id="aclasspcl_1_1_registration_html_adbd6519634f433c0be2fd640c0c75108"><div class="ttname"><a href="classpcl_1_1_registration.html#adbd6519634f433c0be2fd640c0c75108">pcl::Registration::transformation_epsilon_</a></div><div class="ttdeci">double transformation_epsilon_</div><div class="ttdoc">The maximum difference between two consecutive transformations in order to consider convergence (user...</div><div class="ttdef"><b>Definition:</b> registration.h:516</div></div>
<div class="ttc" id="agroup__common_html_ga52d532f7f2b4d7bba78d13701d3a33d8"><div class="ttname"><a href="group__common.html#ga52d532f7f2b4d7bba78d13701d3a33d8">pcl::transformPointCloud</a></div><div class="ttdeci">void transformPointCloud(const pcl::PointCloud&lt; PointT &gt; &amp;cloud_in, pcl::PointCloud&lt; PointT &gt; &amp;cloud_out, const Eigen::Transform&lt; Scalar, 3, Eigen::Affine &gt; &amp;transform, bool copy_all_fields=true)</div><div class="ttdoc">Apply an affine transform defined by an Eigen Transform</div><div class="ttdef"><b>Definition:</b> transforms.hpp:42</div></div>
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